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Business Intelligence, Analytics, Data Science, and AI TEST BANK | 5th Edition| by Ramesh Sharda, Dursun Delen, Efraim Turban |COMPLETE GUIDE With All Chapters [1-11] |GRADE A+

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Business Intelligence, Analytics, Data Science, and AI TEST BANK | 5th Edition| by Ramesh Sharda, Dursun Delen, Efraim Turban Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science 2 Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 16 Chapter 3 Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling 33 Chapter 4 Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization 49 Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 66 Chapter 6 Predictive Analytics II: Text, Web, and Social Media Analytics 83 Chapter 7 Deep Learning and Cognitive Computing 99 Chapter 8 Prescriptive Analytics: Optimization and Simulation 118 Chapter 9 The Landscape of Software Tools and Languages 137 Chapter 10 New AI-Based Trends in Analytics and Data Science 155 Chapter 11 Ethical, Privacy and Managerial Considerations in Analytics 176

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1



Business Intelligence, Analytics, Data Science,
and AI TEST BANK | 5th Edition| by Ramesh
Sharda, Dursun Delen, Efraim Turban
|COMPLETE GUIDE With All Chapters [1-11]
|GRADE A+




TEST BANK

, 2


Chapter 1 An Overview of Business Intelligence, Analytics, and Data
Science
1) Computerized support is used for organizational and managerial decisions that are
responses to external pressures and to take advantage of market opportunities. Answer: TRUE

2) During the early days of analytics, data was often obtained from the domain experts using
manual processes to build mathematical or knowledge-based models. Answer: TRUE

3) Computer applications have moved from transaction processing and monitoring activities
to problem analysis and decision support applications.

Answer: TRUE

4) Business intelligence is a specific term that describes a variety of information technology
and data capturing focused system architectures.

Answer: FALSE

5) The growth in hardware, software, and network capacities were the core contributors to the
proliferation of decision support and analytics systems.

Answer: FALSE

6) Managing data warehouses requires special methods, including parallel and cloud
computing along with Hadoop/Spark.

Answer: TRUE

7) Managing information on operations, customers, internal procedures, and employee
interactions is the domain of cognitive science.

Answer: FALSE

8) Decision support system (DSS), analytics, and management information system (MIS) have
precise definitions agreed to by most practitioners.

Answer: FALSE

9) Of all analytics types (i.e., descriptive, predictive, and prescriptive), data mining is most
closely related to predictive analytics.

Answer: TRUE

10) Major commercial business intelligence (BI) products and services were well established in
the early 1970s.

Answer: FALSE

, 3


11) Information systems that support such transactions as ATM withdrawals, bank deposits,
and cash register scans at the grocery store represent online transaction processing systems
(OLAP).

Answer: FALSE

12) Many business users in the 1980s referred to their mainframes as "the black hole," because
all the information went into it, but little ever came back, and ad hoc real-time querying was
virtually impossible.

Answer: TRUE

13) Data warehouses are intended to work with informational data used for online transaction
processing (OLTP) systems.

Answer: TRUE

14) Successful BI is a tool developed by and developed for the information systems department.

Answer: FALSE

15) The time between new paradigm shifts in information systems and particularly in analytics
has been getting longer.

Answer: FALSE

16) Traditional BI systems use a large volume of static data that has been extracted, cleansed,
and loaded into a data warehouse to produce reports and analyses.

Answer: TRUE

17) Demands for instant, on-demand access to dispersed information decrease as firms
successfully integrate BI into their operations.

Answer: FALSE

18) Simon's decision process involves three major phases: intelligence, design, and choice.

Answer: TRUE

19) The use of dashboards and data visualizations is often very effective in identifying issues
and opportunities in organizations.

Answer: TRUE

20) The use of statistics in baseball by the Oakland Athletics, as described in the Moneyball case
study, is an example of the effectiveness of data analytics.

Answer: TRUE

, 4


21) The intelligence phase of the decision process involves developing a model that represents
the underlying system.

Answer: FALSE

22) The goal of prescriptive analytics is to use data and machine learning tools to forecast the
future values of the variables of interest.

Answer: FALSE

23) Descriptive analytics is often called reporting analytics with the goal of answering the
question of "what happened."

Answer: FALSE

24) In the design phase of the human decision process, the problem or opportunity is defined
based on all available data and information.

Answer: FALSE

25) Business analytics continuum starts with predictive analytics, continues with descriptive
analytics and ends with prescriptive analytics.

Answer: FALSE

26) In the Opening Vignette on Sports Analytics, what was adjusted to drive one- time ticket
sales?

A) Player selections
B) Stadium location
C) Fan tweets
D) Ticket prices
Answer: D

27) In the Opening Vignette on Sports Analytics, what type of modeling was used to predict
offensive tactics?

A) Heuristics
B) Heat maps
C) Cascaded decision trees
D) Sentiment analysis


Answer: B

28) Computer applications have moved from transaction processing and monitoring to other
activities. Which of the following is not one of those activities?

A) Problem analysis
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